Hendrycks  [1]} revealed this model-specific OOD detection problem to the society and proposed a baseline method for detection through the softmax value of the target DNN model.
In recent years, the primary insight of OOD detection is to design a more effective metric than softmax value to distinguish data from in-distribution to OOD.
ODIN [2]} applies the temperature scaling [3]} to pretrained DNN model to enlarge the gap between in-distribution and OOD data, outperforming popular strategies [4]}, , MC-Dropout [5]}, DeepEnsemble [6]}, and PixelCNN++, [7]}.
Mahalanobis [8]} provides a distance-based approach to attain this goal.
They determine the OOD input by computing the Mahalanobis distance between the hidden layer outputs. To improve the detection efficacy,
one often preprocesses the input data.
For example, ODIN borrows the idea of adversarial attack [9]}, adding the perturbation to input to separate the in-distribution and OOD data more clearly.
